Autonomous systems rely on data volume, diversity, and edge-case coverage; however, real-world collection is slow, expensive, and often incomplete. At Synetic AI, we've built a physics-driven, no-gen synthetic data platform that produces photorealistic, behaviorally grounded datasets tuned to AV sensor suites and edge deployment architectures. This talk explores how procedural scene generation, sensor simulation (LiDAR, Radar, IR, RGB), and supervised learning loops enable faster iteration, safer validation, and higher model performance than traditional data pipelines. We'll share insights on where synthetic-trained models fit into real-world ADAS applications and how no-gen synthetic datasets are outperforming real-world data in key perception tasks.